{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d3e03fa3",
   "metadata": {},
   "source": [
    "# 1. 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47956ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brix</th>\n",
       "      <th>wl1</th>\n",
       "      <th>wl2</th>\n",
       "      <th>wl3</th>\n",
       "      <th>wl4</th>\n",
       "      <th>wl5</th>\n",
       "      <th>wl6</th>\n",
       "      <th>wl7</th>\n",
       "      <th>wl8</th>\n",
       "      <th>wl9</th>\n",
       "      <th>...</th>\n",
       "      <th>wl591</th>\n",
       "      <th>wl592</th>\n",
       "      <th>wl593</th>\n",
       "      <th>wl594</th>\n",
       "      <th>wl595</th>\n",
       "      <th>wl596</th>\n",
       "      <th>wl597</th>\n",
       "      <th>wl598</th>\n",
       "      <th>wl599</th>\n",
       "      <th>wl600</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.5</td>\n",
       "      <td>-1.032355</td>\n",
       "      <td>-1.030551</td>\n",
       "      <td>-1.027970</td>\n",
       "      <td>-1.024937</td>\n",
       "      <td>-1.021866</td>\n",
       "      <td>-1.019143</td>\n",
       "      <td>-1.016866</td>\n",
       "      <td>-1.014910</td>\n",
       "      <td>-1.012907</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692447</td>\n",
       "      <td>0.692944</td>\n",
       "      <td>0.692376</td>\n",
       "      <td>0.690764</td>\n",
       "      <td>0.688081</td>\n",
       "      <td>0.684294</td>\n",
       "      <td>0.679348</td>\n",
       "      <td>0.673240</td>\n",
       "      <td>0.665922</td>\n",
       "      <td>0.657307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.7</td>\n",
       "      <td>-1.139034</td>\n",
       "      <td>-1.137186</td>\n",
       "      <td>-1.134485</td>\n",
       "      <td>-1.131222</td>\n",
       "      <td>-1.127761</td>\n",
       "      <td>-1.124464</td>\n",
       "      <td>-1.121508</td>\n",
       "      <td>-1.118802</td>\n",
       "      <td>-1.115973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729328</td>\n",
       "      <td>0.728031</td>\n",
       "      <td>0.725548</td>\n",
       "      <td>0.721815</td>\n",
       "      <td>0.716767</td>\n",
       "      <td>0.710405</td>\n",
       "      <td>0.702706</td>\n",
       "      <td>0.693710</td>\n",
       "      <td>0.683480</td>\n",
       "      <td>0.671960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.1</td>\n",
       "      <td>-1.152821</td>\n",
       "      <td>-1.150937</td>\n",
       "      <td>-1.148288</td>\n",
       "      <td>-1.145165</td>\n",
       "      <td>-1.141951</td>\n",
       "      <td>-1.138977</td>\n",
       "      <td>-1.136366</td>\n",
       "      <td>-1.134011</td>\n",
       "      <td>-1.131516</td>\n",
       "      <td>...</td>\n",
       "      <td>0.736608</td>\n",
       "      <td>0.735214</td>\n",
       "      <td>0.732669</td>\n",
       "      <td>0.728911</td>\n",
       "      <td>0.723844</td>\n",
       "      <td>0.717415</td>\n",
       "      <td>0.709645</td>\n",
       "      <td>0.700555</td>\n",
       "      <td>0.690147</td>\n",
       "      <td>0.678390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.8</td>\n",
       "      <td>-1.087215</td>\n",
       "      <td>-1.085455</td>\n",
       "      <td>-1.082867</td>\n",
       "      <td>-1.079797</td>\n",
       "      <td>-1.076568</td>\n",
       "      <td>-1.073632</td>\n",
       "      <td>-1.071087</td>\n",
       "      <td>-1.068877</td>\n",
       "      <td>-1.066654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.758695</td>\n",
       "      <td>0.757963</td>\n",
       "      <td>0.756038</td>\n",
       "      <td>0.752903</td>\n",
       "      <td>0.748496</td>\n",
       "      <td>0.742886</td>\n",
       "      <td>0.736085</td>\n",
       "      <td>0.728019</td>\n",
       "      <td>0.718577</td>\n",
       "      <td>0.707702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.1</td>\n",
       "      <td>-1.080364</td>\n",
       "      <td>-1.078436</td>\n",
       "      <td>-1.075784</td>\n",
       "      <td>-1.072693</td>\n",
       "      <td>-1.069562</td>\n",
       "      <td>-1.066691</td>\n",
       "      <td>-1.064214</td>\n",
       "      <td>-1.062025</td>\n",
       "      <td>-1.059787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.719793</td>\n",
       "      <td>0.718875</td>\n",
       "      <td>0.716860</td>\n",
       "      <td>0.713771</td>\n",
       "      <td>0.709577</td>\n",
       "      <td>0.704232</td>\n",
       "      <td>0.697717</td>\n",
       "      <td>0.689882</td>\n",
       "      <td>0.680748</td>\n",
       "      <td>0.670263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 601 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Brix       wl1       wl2       wl3       wl4       wl5       wl6       wl7  \\\n",
       "0  15.5 -1.032355 -1.030551 -1.027970 -1.024937 -1.021866 -1.019143 -1.016866   \n",
       "1  16.7 -1.139034 -1.137186 -1.134485 -1.131222 -1.127761 -1.124464 -1.121508   \n",
       "2  18.1 -1.152821 -1.150937 -1.148288 -1.145165 -1.141951 -1.138977 -1.136366   \n",
       "3  14.8 -1.087215 -1.085455 -1.082867 -1.079797 -1.076568 -1.073632 -1.071087   \n",
       "4  15.1 -1.080364 -1.078436 -1.075784 -1.072693 -1.069562 -1.066691 -1.064214   \n",
       "\n",
       "        wl8       wl9  ...     wl591     wl592     wl593     wl594     wl595  \\\n",
       "0 -1.014910 -1.012907  ...  0.692447  0.692944  0.692376  0.690764  0.688081   \n",
       "1 -1.118802 -1.115973  ...  0.729328  0.728031  0.725548  0.721815  0.716767   \n",
       "2 -1.134011 -1.131516  ...  0.736608  0.735214  0.732669  0.728911  0.723844   \n",
       "3 -1.068877 -1.066654  ...  0.758695  0.757963  0.756038  0.752903  0.748496   \n",
       "4 -1.062025 -1.059787  ...  0.719793  0.718875  0.716860  0.713771  0.709577   \n",
       "\n",
       "      wl596     wl597     wl598     wl599     wl600  \n",
       "0  0.684294  0.679348  0.673240  0.665922  0.657307  \n",
       "1  0.710405  0.702706  0.693710  0.683480  0.671960  \n",
       "2  0.717415  0.709645  0.700555  0.690147  0.678390  \n",
       "3  0.742886  0.736085  0.728019  0.718577  0.707702  \n",
       "4  0.704232  0.697717  0.689882  0.680748  0.670263  \n",
       "\n",
       "[5 rows x 601 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入 pandas 读取数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 读取数据\n",
    "data = pd.read_csv(\"./data/peach_spectra_brix.csv\")\n",
    "data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83378b21",
   "metadata": {},
   "source": [
    "# 2. 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0407d8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据矩阵 data.shape： (50, 601)\n",
      "X.shape:(50, 600), y.shape:(50,)\n"
     ]
    }
   ],
   "source": [
    "# m * n \n",
    "print(\"数据矩阵 data.shape：\",data.shape)\n",
    "\n",
    "# 50个样本， 600个 波段 第一列是 桃子糖度值 需要分离开\n",
    "X = data.values[:,1:] \n",
    "# 等同操作\n",
    "#X = data.drop(['Brix'], axis=1)\n",
    "\n",
    "y = data.values[:,0]\n",
    "# 等同操作\n",
    "# y = data.loc[:,'Brix'].values\n",
    "\n",
    "print(f\"X.shape:{X.shape}, y.shape:{y.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc925958",
   "metadata": {},
   "source": [
    "# 3. 工具导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1bec6331",
   "metadata": {},
   "outputs": [],
   "source": [
    "import CARS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62815cc8",
   "metadata": {},
   "source": [
    "# 4. 建模筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a89d129b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "获取波段数： 41\n",
      "[148 166 197 198 223 224 242 252 253 256 295 296 297 298 311 312 322 325\n",
      " 345 377 382 392 410 411 415 426 427 428 429 430 471 472 479 494 495 539\n",
      " 549 553 584 589 590]\n"
     ]
    }
   ],
   "source": [
    "lis = CARS.CARS_Cloud(X,y)\n",
    "print(\"获取波段数：\",len(lis))\n",
    "print(lis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c5d479",
   "metadata": {},
   "source": [
    "# 5. 导出数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "68134595",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = X[:,lis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc8589da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 41)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e45c5e7b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
